Hylke Jan Glass

Efficient processing of gold from generally low-grade ore integrates the deportment of gold into appropriate process design and, during operation of a Carbon-In-Leach (CIL) plant, control of ore fed to the plant, the passage of slurry and activated carbon through the plant, and the dosage of cyanide. A sampling campaign in an operating CIL plant shows that, despite variability in head grade and sampling uncertainty, the recovery of gold is well over 90 %. This process resilience is achieved through a fit-for-purpose process design, a relatively long residence time, and excess of cyanide. Process optimisation requires careful consideration to avoid loss of gold to tailings.

Geometallurgy seeks to derisk the extraction of primary resources by developing optimal strategies across resource/reserve evaluation, mine planning, mineral processing, environmental management (including waste management), and energy use. Predictive geometallurgy is the crux of modern geometallurgical practice, which leads to a data-rich 3D block model(s). The geometallurgical approach aims to quantify variability through different sample types, including in situ and direct measurements; physical samples; process samples within the plant; and in-line sensor-based measurements. Sampling considerations across sample type, representativity, number of samples required, sample integrity, Quality Assurance/Quality Control, and reporting results in the context of international codes are emphasised. A geometallurgical protocol was developed to obtain multivariate data for highly heterogeneous gold-bearing conglomerate mineralisation. The protocol emphasises the importance of collecting high-quality samples through the use of diamond drill core and early implementation. The programme aimed to acquire an accurate head grade of each core intersection prior to destruction by metallurgical testwork. Core scanning and comminution test work was undertaken prior to the head assay. The protocol was developed so as to allow each core interval to be submitted for comminution testwork, recombined for head grade determination by PhotonAssay™, and subsequently submitted for gold recovery testwork. All core was scanned prior to testwork and assay, which collected digital imagery, geochemistry, and bulk density data. A comprehensive quality assurance and quality control system was implemented for the programme. This paper presents an overview of geometallurgical sampling and the development and implementation of the Beatons Creek testwork programme in support of a Pre-Feasibility Study.

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can incorporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p < 10 × 10⁻⁵), with correlation coefficients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency.

The underground mining process typically results in some of the metal inventory remaining as a broken residue within mine workings. Up to 0.5 m of broken ore may be left on the floors of development drives and in stopes. It is possible that this broken ore contains 5% or more of the original metal in the ore reserve, which will have a material effect on reconciliation and project economics. Broken ore remaining in the mine may have been subject to enhanced milling during the mucking process, yielding enhanced liberation of the economic minerals of interest. Given that the material in question is already broken, the sampling strategy will be based on digging trenches or pits into the mine floor to extract a pre-determined mass of material for assay. The sampling of stope floors will most likely be based on grab sampling. Application of the theory of sampling is a key aspect of ensuring that evaluation is effective. Gy’s equation for the fundamental sampling error can be used to determine an optimum sample mass, and to inform subsequent steps in preparation for assaying at given confidence limits and precision. This paper presents a discussion and case study.

Cobalt (Co) mine production primarily originates from the sediment-hosted copper (Cu) deposits of the Democratic Republic of Congo (DRC). These deposits usually consist of three ore zones with a supergene oxide ore blanket overlying a transition zone which grades into a sulphide zone at depth. Each of these zones display a mineral assemblage with varying gangue mineralogy and, most importantly, a distinct state of oxidation of the mineralisation. This has direct implications for Cu and Co extraction during mineral processing as it dictates which processing method is to be used (i.e., leaching vs. flotation) and affects the performance of these. To optimise resource efficiency, reduce technical risks and environmental impacts, comprehensive understanding of variation of ore mineralogy and texture in the deposit is essential. By defining geometallurgical ore types according to their inferred metallurgical behaviour, this information can serve to classify the resources and improve resource management. To obtain insight into the spatial distribution of mineral grades, it is necessary to develop techniques that have the potential to measure rapidly and, preferably, within the mine at relatively low-cost. In this study, the application of portable Fourier transformed infrared (FTIR) spectroscopy is investigated to measure the mineralogy of drill core samples. A set of samples from a sediment-hosted Cu-Co deposit in DRC was selected to test this approach. Results were validated using automated mineralogy (QEMSCAN). Prediction of gangue and target mineral grades from the FTIR spectra was achieved through partial least squares regression (PLS-R) combined with competitive adaptive reweighted sampling (CARS). It is shown that the modal mineralogy obtained from FTIR can be used to classify the ore according to type of mineralisation and gangue mineralogy into geometallurgical ore types. This classification supports selection of a suitable processing route and is likely to affect the overall process performance.

Sampling, sample preparation, and assay protocols aim to achieve an acceptable estimation variance, as expressed by a relatively low nugget variance compared to the sill of the variogram. With gold ore, the typical heterogeneity and low grade generally indicate that a large sample size is required, and the effectiveness of the sampling protocol merits attention. While sampling protocols can be optimised using the Theory of Sampling, this requires determination of the liberation diameter (d_ℓAu) of gold, which is linked to the size of the gold particles present. In practice, the liberation diameter of gold is often represented by the most influential particle size fraction, which is the coarsest size. It is important to understand the occurrence of gold particle clustering and the proportion of coarse versus fine gold. This paper presents a case study from the former high-grade Crystal Hill mine, Australia. Visible gold-bearing laminated quartz vein (LV) ore was scanned using X-ray computed micro-tomography (XCT). Gold particle size and its distribution in the context of liberation diameter and clustering was investigated. A combined mineralogical and metallurgical test programme identified a liberation diameter value of 850 µm for run of mine (ROM) ore. XCT data were integrated with field observations to define gold particle clusters, which ranged from 3–5 mm equivalent spherical diameter in ROM ore to >10 mm for very high-grade ore. For ROM ore with clusters of gold particles, a representative sample mass is estimated to be 45 kg. For very-high grade ore, this rises to 500 kg or more. An optimised grade control sampling protocol is recommended based on 11 kg panel samples taken proportionally across 0.7 m of LV, which provides 44 kg across four mine faces. An assay protocol using the PhotonAssay technique is recommended.

Clustering approaches are widely used to group similar objects and facilitate problem analysis and decision-making in many fields. During short-term planning of open-pit mines, clustering aims to aggregate similar blocks based on their attributes (e.g., geochemical grades, rock types, geometallurgical parameters) while honoring various constraints: i.e., cluster shapes, size, alignment with mining direction, destination, and rock type homogeneity. This approach helps to reduce the computational cost of optimizing short-term mine plans. Previous studies have presented ways to perform clustering without honoring constraints specific to mining. This paper presents a novel block clustering heuristic capable of considering and honoring a set of mining block aggregation requirements and constraints. Constraints can relate to the clustering adjacent blocks, achieving higher destination homogeneities, controlled cluster size, consistency with mining direction, and achieving clusters with mineable shapes and rock types’ homogeneity. The proposed algorithm’s application on two different datasets demonstrates its efficiency and capability in generating reasonable block clusters while meeting different predefined aggregation requirements and constraints.